Affordable Housing as a Profitable Impact Investment (eBook)
XVIII, 122 Seiten
Springer International Publishing (Verlag)
978-3-031-07091-4 (ISBN)
The challenges of affordable housing are manifold. However this presents an opportunity to private investors, real estate companies, and developers. With the growing global trend for impact-based investments, many institutional investors have begun to consider the merits of this asset class.
This book examines not only the profitability of these assets, but also whether these assets rely on government subsidy. It discusses why investors have become more interested in this product and which investment criteria influence the financial performance of these assets. The authors employed a mixed method approach to collect data at two tiers, first through surveys and afterwards through interviews of 8 firms (3 publicly listed companies, 3 private equity companies, 1 foundation, and 1 state bank) across Germany, the United Kingdom, and the United States. Investment criteria are analyzed using inferential statistics, specifically the Hierarchical Algorithm Cluster Analysis. The financial characteristics of the companies are examined and compared using descriptive statistics and the qualitative interview output is explored using the thematic Latent Coding Analysis.
Furthermore, the book explains how the bond-like nature of affordable housing is a profitable impact investment option, and how this strategy is particularly worthwhile for institutional investors. It also describes that profitability of affordable housing products is not dependent on subsidy. Still, affordable housing products supported by government incentives in the United Kingdom and United States are most attractive. The book illustrates six important investment strategies identified by veterans in this field to have an influence on the financial feasibility of affordable housing products.
Deepiga Vigneswaran is an investment professional with experience in the alternative investments space, including pension asset management, real estate asset management, and conducting due diligence of funds and direct opportunities. She currently works in the investment team at EBG Investment Solutions AG in Zurich (Switzerland), a globally active boutique firm advising and managing sustainable private market portfolios for institutional investors.
Michael Truebestein is Professor of Real Estate Management and Investments and Head of the Master of Science in Real Estate program at the University of Lucerne (HSLU) in Switzerland. Furthermore, he was elected president of the RICS in Switzerland in 2020. Michael Truebestein is author of various publications in the fields of real estate strategies, real estate investments, asset management, real estate markets and digitization. In 2014, he initiated the annual study on 'Real Estate Investments and Asset Management' of institutional investors (in cooperation with the ASIP / Swiss Pension Fund Association) and carried out the world's first tokenization of a property in 2019, the 'Hello World' in Baar (Switzerland). In 2021, the media elected him to be part of the 100 leading and most important people in the Swiss Real Estate industry.
Matthias Daniel Aepli is a Lecturer at the University of Lucerne (HSLU) in Switzerland with experience in real estate management and investments, corporate finance and risk management. In addition to lecturing in the Master of Science programs in Real Estate as well as International Financial Management and Banking and Finance he is a serial entrepreneur in the real estate sector.
Erscheint lt. Verlag | 26.8.2022 |
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Reihe/Serie | Contributions to Finance and Accounting | Contributions to Finance and Accounting |
Zusatzinfo | XVIII, 122 p. 36 illus., 33 illus. in color. |
Sprache | englisch |
Themenwelt | Wirtschaft ► Betriebswirtschaft / Management |
Schlagworte | Hierarchical Algorithm Cluster Analysis • Housing • investment strategies • Latent Coding Analysis • real estate economics • Real Estate Management • Subsidies |
ISBN-10 | 3-031-07091-7 / 3031070917 |
ISBN-13 | 978-3-031-07091-4 / 9783031070914 |
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